Synth-Empathy: Towards High-Quality Synthetic Empathy Data
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909284220010496 |
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| author | Liang, Hao Sun, Linzhuang Wei, Jingxuan Huang, Xijie Sun, Linkun Yu, Bihui He, Conghui Zhang, Wentao |
| author_facet | Liang, Hao Sun, Linzhuang Wei, Jingxuan Huang, Xijie Sun, Linkun Yu, Bihui He, Conghui Zhang, Wentao |
| contents | In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite. Consequently, managing and understanding empathetic datasets have gained increasing significance. However, empathetic data are typically human-labeled, leading to insufficient datasets and wasted human labor. In this work, we present Synth-Empathy, an LLM-based data generation and quality and diversity selection pipeline that automatically generates high-quality empathetic data while discarding low-quality data. With the data generated from a low empathetic model, we are able to further improve empathetic response performance and achieve state-of-the-art (SoTA) results across multiple benchmarks. Moreover, our model achieves SoTA performance on various human evaluation benchmarks, demonstrating its effectiveness and robustness in real-world applications. Furthermore, we show the trade-off between data quantity and quality, providing insights into empathetic data generation and selection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21669 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synth-Empathy: Towards High-Quality Synthetic Empathy Data Liang, Hao Sun, Linzhuang Wei, Jingxuan Huang, Xijie Sun, Linkun Yu, Bihui He, Conghui Zhang, Wentao Computation and Language Machine Learning In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite. Consequently, managing and understanding empathetic datasets have gained increasing significance. However, empathetic data are typically human-labeled, leading to insufficient datasets and wasted human labor. In this work, we present Synth-Empathy, an LLM-based data generation and quality and diversity selection pipeline that automatically generates high-quality empathetic data while discarding low-quality data. With the data generated from a low empathetic model, we are able to further improve empathetic response performance and achieve state-of-the-art (SoTA) results across multiple benchmarks. Moreover, our model achieves SoTA performance on various human evaluation benchmarks, demonstrating its effectiveness and robustness in real-world applications. Furthermore, we show the trade-off between data quantity and quality, providing insights into empathetic data generation and selection. |
| title | Synth-Empathy: Towards High-Quality Synthetic Empathy Data |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.21669 |